Strong interference has become a common problem as the radio spectrum has become more crowded. Constant envelope, or approximately constant envelope signals are popular because such waveforms are compatible with non-linear amplifiers which can be more energy efficient than linear amplifiers. Examples of constant envelope signals include: frequency modulation, frequency shift keying, minimum shift keying, Gaussian minimum shift keying, multi-h continuous phase frequency modulation, linear FM, continuous wave, and many frequency hopping signals. Any of these types of constant envelope signals can cause interference with other, desired signals, particularly where the desired signal and the constant envelope signal spectrally overlap with one another.
Because constant envelope interference is wide-spread in practical applications, numerous approaches have been devised to mitigate strong constant envelope co-channel interference received using a single receive antenna. Maximum likelihood sequence estimation (MLSE) in the presence of constant envelope interference is one known technique with a reasonably simple hardware implementation. See, for example, Hui et al., “Maximum Likelihood Sequence Estimation in the Presence of Constant Envelope Interference,” IEEE Vehicular Technology Conference 2: 1060-1064 (2003), the entire contents of which are incorporated by reference herein. However, the MLSE algorithm or hardware must be customized for the specific desired signal.
Another approach uses an adaptive filter to cancel interference caused by a constant envelope signal. This adaptive approach requires time to converge, and even then a narrow band signal buried beneath a wide-band strong interference signal might not be recovered because the steady state transfer function is frequency selective. See, for example, Ferrara, “A Method for Cancelling Interference from a Constant Envelope Signal,” IEEE Transactions on Acoustics, Speech, and Signal Processing 33(1): 316-319 (1985), the entire contents of which are incorporated by reference herein.
A different approach maps a complex received signal into polar coordinates. Then a fast Fourier transform (FFT) is computed on a block of magnitude samples. The spectrum of the magnitude samples is then excised. An inverse FFT (iFFT) then transforms the excised spectrum into the time-domain. Such an approach does not require convergence time or any parameters of the weak signal, and can cancel many interference signals automatically. See, for example, Henttu, “A New Interference Suppression Algorithm Against Broadband Constant Envelope Interference,” IEEE Milcom 2: 742-746 (2000), the entire contents of which are incorporated by reference herein. However, such an approach can be computationally complex.
Or, for example, successive interference cancellation can require demodulating the undesired interference signal in order to subtract the interference. Joint demodulators can mitigate interference by demodulating both signals together in a statistically optimum manner such as least squares estimation. In either case, a demodulator for one desired signal type can then require demodulators for many different undesired signal types. As new signals emerge then algorithms must be updated. Unknown signals, such as proprietary waveforms, might render successive interference cancellation or joint demodulators impractical.
Thus, what is needed are improved systems and methods for reducing constant envelope interference.